Related Experiment Video
Updated: May 14, 2025
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
AI in clinical decision-making: ChatGPT-4 vs. Llama2 for otolaryngology cases
Antonino Maniaci1,2,3,4, Cosima C Hoch5,6, Lise Sogalow5,7
1Department of Medical and Surgical Sciences, Faculty of Medicine, University of Enna Kore, Enna, Italy. Antonino.maniaci@unikore.it.
Purpose:
To evaluate the diagnostic accuracy, appropriateness of additional examination recommendations, and consistency of therapeutic regimens by ChatGPT-4 and Llama2 based on real otolaryngology cases.
Methods:
A prospective controlled study was conducted on 98 anonymized otolaryngology cases. Clinical information was entered in ChatGPT-4 and Llama2 for reaching primary diagnoses, additional examination recommendations, and treatment strategies. Two independent otolaryngologists evaluated the AI outputs using the artificial intelligence performance instrument (AIPI), evaluating diagnostic accuracy, appropriateness of examination, and adequacy of treatment. Statistical comparisons were conducted between the AI systems and expert decisions. Interrater reliability was evaluated with kappa statistics.
Results:
ChatGPT-4 diagnosed 82% correctly, outperforming Llama2 at 76%. For additional examinations, ChatGPT-4 suggested relevant and appropriate tests in 88% of the studies, while Llama2 did so in 83%. Treatment appropriateness was achieved in 80% of the cases through ChatGPT-4 and 72% through Llama2. Sometimes, both systems suggested inappropriate tests. The interrater reliability was high for AIPI scores (kappa = 0.85).
Conclusion:
ChatGPT-4 and Llama2 have shown great potential as clinical decision-support tools in otolaryngology, with ChatGPT-4 exhibiting superior performance. At the same time, non-relevant recommendations indicate further refinement and human oversight to ensure safe application in clinical practice.
